Dockerizing a Python Flask Application with a Database

Last Updated : 28 Apr, 2025

Applications are packaged, distributed, and run via Docker within Docker containers throughout the process of "dockerizing". The exact environment and dependencies must be installed in order to run that application on another local machine when it is developed under a specific environment and dependencies. As a result, it is very difficult to proceed on another local machine. This is where docker comes for containerizing applications. Flask web applications will be deployed inside of Docker containers since they don't require a specific system environment to run. it consists of two containers one is for flask and the other is for database. these containers are connected using "docker-compose.yml" file. here we are going to use database mongoDB.

Key Terminologies

The Dockerized Flask application consists of :

Flask: it is micro web framework based on python programming language used for eveloping backend REST API microservices.
MongoDB: it is a Document type key-value pair NOSQL database, stores data in json format.
Docker: it is used to build, test, and deploy applications
Containerization: it is technique used to packing the application's dependencies into one or more containers.
App.py : where the main python(Flask server) code is written
Templates: where html and css (front-end) files are stored
Reqirements.txt : the requirements to run the appliction are written
Dockerfile: used to build docker image
Docker-compose.yml : used to configure services, networks, connection, volumes

Step-By-Step Process

1. Create A Directory

Start by creating a directory for your project. In this example, we'll name it

mkdir <Name Of the Directory> (flask_to_do_list)

open directory in any IDE , i am using VS Code

2. Create The Flask Application

Here, I am going to create a simple to do list flask application where i can add and remove daily tasks

app.py:

Python3

from flask import Flask, render_template, request, redirect, url_for
from pymongo import MongoClient
from bson import ObjectId

app = Flask(__name__)

client = MongoClient(host='test_mongodb',port=27017, username='root', password='pass',authSource="admin")
db = client.mytododb
tasks_collection = db.tasks

@app.route('/')
def index():
    tasks = tasks_collection.find()
    return render_template('index.html', tasks=tasks)

@app.route('/add_task', methods=['POST'])
def add_task():
    task_name = request.form.get('task_name')
    if task_name:
        tasks_collection.insert_one({'name': task_name})
    return redirect(url_for('index'))

@app.route('/delete_task/<task_id>', methods=['GET'])
def delete_task(task_id):
    tasks_collection.delete_one({'_id': ObjectId(task_id)})
    return redirect(url_for('index'))

if __name__ == '__main__':
    app.run(host='0.0.0.0',debug=True)

3. Set Up Front-End

Create another directory named templates, it consists of front-end (html) codes.inside templates directory index.html:

HTML

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Todo List</title>
</head>
<body>
    <h1>Todo List</h1>
    <form action="/add_task" method="POST">
        <input type="text" name="task_name" placeholder="Task name">
        <button type="submit">Add Task</button>
    </form>
    <ul>
        {% for task in tasks %}
            <li>{{ task.name }} <a href="/delete_task/{{ task._id }}">Delete</a></li>
        {% endfor %}
    </ul>
</body>
</html>

now we can run our flask application if the dependencies are installed on particular system.

4. Create a Dockerfile

Dockerfile is used to specify base image, copying the application code to container, set up environment and dependencies.create file named "Dockerfile" without any dot extension at the end insert the following code into it.

Dockerfile :

FROM python:3.6
ADD . /app
WORKDIR /app
RUN pip install -r requirements.txt

6. Configure docker-compose.yml

The docker-compose.yml file is used to define services , networks ,ports, connection between containers and volumes. The ports are given to Flask app is 5000 and for database is 27017

docker-compose.yml :

web:
  build: .
  command: python -u app.py
  ports:
    - "5000:5000"
  volumes:
    - .:/app
  links:
    - db
db:
  image: mongo:latest
  hostname: test_mongodb
  environment:
    - MONGO_INITDB_DATABASE=animal_db
    - MONGO_INITDB_ROOT_USERNAME=root
    - MONGO_INITDB_ROOT_PASSWORD=pass
  ports:
    - 27017:27017

7. Define Dependencies

Requirements.txt is used to install the dependencies for application in the image to run in the container.

requirements.txt:

flask

pymongo

Now all the files are created and their code is also written. The directory structure in the VS Code:

8. Build Docker Images

In your terminal, navigate to the project directory and build the Docker image of the Flask application with MongoDB by running:

sudo docker-compose up

All Set, building of images is done, now we have to start pulling the images and start containers.

9. Running Docker Container

Make new terminal and run the following command to build the docker image of flask application with database MongoDB:

sudo docker-compose up

Running Docker COntainer

you can also refer to screenshots below for more clarity

Running Docker

9. Test the Application

You can click on the link generated in the terminal after running of application image to test application on browser which is running inside the docker containers.Testing our application on browser with the link format is "http://<docker-machine-ip>:5000/" my docker machine ip is 172.17.0.3 then the link becomes: http://172.17.0.3:5000/

Application Test

All done, the Dockerized Python Flask Application with a Database MongoDB is running in the container of docker and you can access it on the localhost port 5000 with system's ip addresss

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Article Tags :

Dockerizing a Python Flask Application with a Database

Key Terminologies

Step-By-Step Process

1. Create A Directory

2. Create The Flask Application

3. Set Up Front-End

4. Create a Dockerfile

6. Configure docker-compose.yml

7. Define Dependencies

8. Build Docker Images

9. Test the Application

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Monitoring and Logging

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